The initial aims of the project were to identify characteristics of the command process at Higher Command levels in the Royal Navy so that systems design could include decision aids at this important focal point"in the overall system (a naval Task Force). A prerequisite for formulating recommendations is an understanding "of the Task Force Commander's role and decision process. Consequently, an attempt was made early on to structure the Task Force command task. It became apparent that, without special measures, such a description could not be acquired. This thesis, therefore, is the story of a project about collecting data and informa tion, using it and then interpret ting it for the aboye purposes. Since there was little known about how to achieve an understanding of senior decision making, especially in potentially unstructured areas like the naval Higher Command function in a conflict environment, appropriate measures were developed to do so. The overall methodology designed consisted of: a) basic research; b) interview techniques; c) scientific gaming procedure; and d) a data collection-in-action regime. The methodology sought to use 'laboratory' techniques initially to acquire enough insight to then mount a study of clients "in action". It was considered necessary to supplement the former types of methods (interviews and gaming activities) with versions actually involving the clients when performing their role (in the form of a structured self-report study). The overall methodology was rooted in the systems ideas of Checkland (1981) and Bowen's views on the OR process (1981 and 1984) . Both authors react against the traditional prescriptive, normative approach of text book OR practitioners. The latter pursui t ignores messy, human aspects of organisational life and requires that a problem situation is well understood so that applied mathematical techniques can be used to formulate and then , solve' a problem. Most high-level decision problems, though, cannot be so formulated. This thesis is an attempt to formulate and understand high-level decision problems in a different way, using different techniques, but with a similar aim of arriving at useful and meaningful decision support recommendations. The methods to be described should start to fill the gap that exists at the moment in the OR repertoire of methodology catering for such needs. It was not possible to implement the overall methodology in the naval context. The industrial phase of the research was therefore initiated so that all of the phases could be tested in an albeit limited programme. Some preliminary insights and findings emerged in both contexts. Essentially, high-level decision makers approach their tasks differently. They have differing priorities also - as revealed by the interview and gaming phases. However, the data collection-in-action study (implemented in an industrial context only) revealed that what happens in reality (when they are at work) does not fully reflect the pattern of priorities revealed in the laboratory studies. Consequently, it is necessary to use other means to arrive at a complete picture of their decision making process. The overall methodology includes the interview and gaming phases because they are necessary to acquire enough insights to mount a data collection-in-action study later on. Also, they have other research and training uses. The ~hesis also includes the use of an analysis technique (based on Hogberg 1985) which assists with the appreciation of high-level decision making problems. The technique forms the basis of a proposed decision support system for both military and industrial contexts.

Information systems and in particular decision support systems have been developed to supplement human information processing and to assist with decision-making. Human decision-making is facilitated by the often unconscious use of heuristics or rules of thumb in situations where it may not be possible or feasible to search for the best decision. Judgemental heuristics have previously been found to lead to biases in decision-making. When information systems are used as decision aids, they may have an influence on biases. This study investigates the possible role of information systems in introducing, reinforcing or reducing biases of decision-making. It has been found that information systems have the ability to introduce new biases and to reinforce biases. Information systems can also reduce biases, but this requires innovate thinking on the way information is represented and the way human decision-making processes are supported. It has also been found that in the real world, other than the laboratories where biases are usually measured, other constraints on rational decision-making, such as politics or data errors, can overshadow the effects of biases. / Dissertation (MPhil)--University of Pretoria, 2003. / Informatics / unrestricted

Agent-based models (ABM) are an increasingly important research tool for describing and predicting interactions among humans and their environment. A key challenge for such models is the ability to faithfully represent human decision making with respect to observed behaviour. This thesis aims to address this challenge by developing a methodology for empirical measurement and simulation of decision making in humanenvironment systems. The methodology employs the Beliefs-Desires-Intentions (BDI) model of human reasoning to directly translate empirically measured decision data into artificial agents, based on sound theoretical principles. A common simulated decision environment is used for both eliciting human decision making behaviour, and validating artificial agents. Using this approach facilitates the collection of decision making narratives by way of participatory simulation, and promotes a fair comparison of real and modelled decision making. The methodology is applied in two case studies: One to carry out a trial involving human subjects solving an abstract land-use problem, and another to examine the feasibility of up-scaling the methodology to a real agricultural scenario—dairy farming. Results from the experiments indicate that the BDI-based methodology achieved reasonably direct encoding of decision making behaviour from elicited human narratives. The main limitations found with the technique are: (1) the significant use of subjects’ time required to elicit their decision making behaviour; (2) the significant programming effort required; and (3) the challenge of aggregating behaviour from multiple subjects into a generalised decision making model. In spite of its limitations, BDI has shown its strengths as a tool for empirical analysis and simulation of decision making in research of human-environment systems.

Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. Dianne Speake, Florida State University, School of Nursing. Title and description from dissertation home page (viewed Sept. 28, 2004). Includes bibliographical references.

Achieving sustainable development to meet the needs of current and future generations is currently on top of the global agenda, both in scientific research as well as global politics. However, achieving sustainable development is still a grand challenge, not least because it is embedded in the context of global change that affects most resource use systems worldwide in multiple ways. Even though many approaches to sustainable management do consider the connection between human activity and environmental dynamics, the role of human behavior as a main driver of system dynamics in coupled human and natural systems is often only poorly addressed. In this thesis, we aim to contribute to an improved understanding under which conditions human resource use decisions lead to sustainable outcomes, with regard to global change. For this, we will take the perspective of human decision-making and its social, ecological and economic consequences in two different resource use contexts, namely a) pastoralism in drylands and b) disaster risk management with respect to floods. We explicitly consider individual human decision-making as driver of social-ecological system dynamics, investigate the feedbacks between system components, as well as the impact of global change on resource use. To analyze such complex system dynamics, simulation models have proven to be helpful analysis tools. Particularly agent-based modeling represents a flexible and powerful analysis tool, as it allows us to model the decisions and interactions of individual agents at the micro level, while at the same time observing the outcome of their behavior on a system level. Within three case studies, we develop agent-based simulation models that capture the dynamics and feedbacks of the social-ecological system under consideration in a spatially explicit way. The first study analyzes the performance of disaster management organizations under change. In the second study, we aim to detect the drivers for polarization in a pastoral system in Morocco. The last study investigates behavioral change of pastoralist households and its impact on social, ecological and economic outcome measures. By analyzing a range of scenarios in each study, we determine both the long-term impact of different decision regimes on the state of the social-ecological system as well as the dimensions of change that have the most profound impact on the system dynamics and the sustainability of resource use. Main results that could be obtained from the modeling experiments include the identification of key resources that have a high influence on the long-term system dynamics. We are also able to show that under the influence of global change, access to certain resources gains in importance, as resources can act as buffer mechanisms to mitigate the adverse effects of global change. Through the operationalization of behavioral theories in model rules and the explicit representation of heterogeneous agent decision making, we could determine under which conditions a more refined representation of human decision making matters, and when a change in behavioral strategies leads to different social-ecological outcomes. Furthermore, all three modeling studies demonstrate the usefulness of stylized agent-based models to gain insights into complex systems. Overall, this thesis contributes to social-ecological systems research by developing appropriate simulation models to address the problem of sustainable resource use under global change.

No description available.

The purpose of this research was to determine whether changes in the presentation format of items in a computer display could be used to alter the impact of specific cognitive biases, and to add to the knowledge needed to construct theory-based guidelines for output design. The problem motivating this study is twofold. The first part of the problem is the sub-optimal decision making caused by the use of heuristics and their associated cognitive biases. The second part of the problem is the lack of a theoretical basis to guide the design of information presentation formats to counter the effects of such biases. An availability model of the impact of changes in presentation format on biases and heuristics was constructed based on the findings of a literature review. A six-part laboratory experiment was conducted utilizing a sample of 205 student subjects from the college of business. The independent variable was presentation format which was manipulated by altering the visual salience or visual recency of items of information in a visual computer display. The dependent variables included recall, perceived importance, and the subjects' responses to three judgment tasks. The results clearly demonstrate that changes in presentation format can be used to alter the impact of cognitive biases on human decision making. The results also provide support for the availability model, with the exception of the proposed influence of learning style. Learning style was found to have no significant impact on decision making whether alone or in combination with changes in presentation format. The results of this investigation demonstrate that by using our knowledge of cognitive processes (e.g., the visual salience effect, the visual recency effect, and the availability heuristic), presentation formats can be altered in order to moderate the effects of certain biases and heuristics in human decision making. An understanding of these results may be useful in improving DSS design.

Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years. Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion. It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed. As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation.

The increasing demand for food and fiber, the need for climate change mitigation and adaptation as well as for environmental protection impose severe challenges on land systems worldwide. Solutions to support the transformation towards a sustainable development of land systems are needed. One response to the multiple challenges is the introduction of policy options aimed at steering land use activities towards a bundle of societal goals. However, it is difficult to empirically foresee the effectiveness and unintended consequences of policy options prior to their deployment. A second response is environmental education because human consumption behavior, among other factors, strongly influences natural ecosystems. However, it is a non-trivial task to develop effective communication strategies for complex topics such as sustainable land management. In both cases, modeling can help to overcome the different obstacles along the way. In this thesis, dynamic process-based social-ecological models at the individual scale are developed and analyzed to study effectiveness and unintended side effects of policy options, which promote agricultural management strategies and were intentionally designed to cope with multiple societal challenges. Two case studies of political intervention are investigated: the promotion of perennial woody crops in European agricultural landscapes for a sustainable bioeconomy and governmental supplementary feeding programs to cope with climate risks in pastoral systems in drylands. These two case studies are complemented by the development of a serious online game on sustainable land management in general that bridges the gap between land use modeling and environmental education. Simulation results of this thesis provide insights into (i) the performance of the politically promoted agricultural management strategies in meeting various intended goals such as poverty alleviation or the maintenance of biodiversity and ecosystem services, (ii) the emergence of unintended (environmental and social) side effects such as land use conflicts, land degradation or cost explosion and (iii) the mitigation of such side effects by appropriately adjusting the design of the policy options. These insights are enabled by representing temporal as well as spatial variability in the developed models. Furthermore, different mechanistic approaches of transferability analyses based on stylized landscapes are developed and applied. They enable to check whether and in what respect policy impacts actually differ substantially between regional contexts, to identify what regional factors steer the impact and to derive indicators for grouping regions of similar policy impacts. Finally, based on a conducted survey-based evaluation and experiences from various applications, the value of the developed serious game for environmental education is revealed and discussed.Altogether, this thesis contributes to model-based decision support for steering transformation towards the sustainable development of land systems in an appropriate way. This is done by developing appropriate social-ecological modeling approaches, by performing specific policy impact analyses in two transformative agricultural systems using these models and by providing a model-based communication tool for environmental education.

This thesis considers control systems governed by autonomous decision-makers and humans. We formalise and compute low-complex control policies with applications to large-scale systems, and propose human interaction models for controllers to compute interaction-aware decisions. In the first part of the thesis, we consider complexity-aware decision-making, formalising the complexity of control policies and constructing algorithms that compute low-complexity control policies. More precisely, first, we consider large-scale control systems given by hierarchical finite state machines (HFSMs) and present a planning algorithm for such systems that exploits the hierarchy to compute optimal policies efficiently. The algorithm can also handle changes in the system with ease. We prove these properties and conduct simulations on HFSMs with up to 2 million states, including a robot application, where our algorithm outperforms both Dijkstra's algorithm and Contraction Hierarchies.  Second, we present a planning objective for control systems modelled as finite state machines yielding an explicit trade-off between a policy's performance and complexity. We consider Kolmogorov complexity since it captures the ultimate compression of an object on a universal Turing machine. We prove that this trade-off is hard to optimise in the sense that dynamic programming is infeasible. Nonetheless, we present two heuristic algorithms obtaining low-complexity policies and evaluate the algorithms on a simple navigation task for a mobile robot, where we obtain low-complexity policies that concur with intuition.  In the second part of the thesis, we consider human-in-the-loop systems and predict human decision-making in such systems. First, we look at how the interaction between a robot and a human in a control system can be predicted using game theory, focusing on an autonomous truck platoon interacting with a human-driven car. The interaction is modelled as a hierarchical dynamic game, where the hierarchical decomposition is temporal with a high-fidelity tactical horizon predicting immediate interactions and a low-fidelity strategic horizon estimating long-term behaviour. The game enables feasible computations validated through simulations yielding situation-aware behaviour with natural and safe interactions.  Second, we seek models to explain human decision-making, focusing on driver overtaking scenarios. The overtaking problem is formalised as a decision problem with perceptual uncertainty. We propose and numerically analyse risk-agnostic and risk-aware decision models, judging if an overtaking is desirable. We show how a driver's decision time and confidence level can be characterised through two model parameters, which collectively represent human risk-taking behaviour. We detail an experimental testbed for evaluating the decision-making process in the overtaking scenario and present some preliminary experimental results from two human drivers. / Denna avhandling studerar styrsystem med autonoma beslutsfattare och människor. Vi formaliserar och beräknar styrlagar av låg komplexitet med tillämpningar på storskaliga system samt föreslår modeller för mänsklig interaktion som kan användas av regulatorer för att beräkna interaktionsmedvetna beslut. I den första delen av denna avhandling studerar vi komplexitet-medveten beslutsfattning, där vi formaliserar styrlagars komplexitet samt konstruerar algoritmer som beräknar styrlagar med låg komplexitet. Mer precist, först studerar vi storskaliga system givna av hierarkiska finita tillståndsmaskiner (HFSMs) och presenterar en planeringsalgoritm för sådana system som utnyttjar hierarkin för att beräkna optimala styrlagar effektivt. Algoritmen kan också lätt hantera förändringar i systemet. Vi bevisar dessa egenskaper och utför simuleringar på HFSMs med upp till 2 miljoner tillstånd, inklusive en robot-applikation, där vår algorithm överträffar både Dijkstra's algoritm och så kallade Contraction Hierarchies. För det andra så presenterar vi ett planeringsobjektiv för finita tillståndsmaskiner som ger en explicit avvägning mellan ett styrlags prestanda och komplexitet. Vi använder Kolmogorovkomplexitet då den fångar den ultimata komprimeringen av ett objekt i en universell Turing-maskin. Vi bevisar att detta objektiv är icke-trivial att optimera över i avseendet att dynamisk programming är omöjligt att utföra. Vi presenterar två algoritmer som beräknar styrlagar med låg komplexitet och evaluerar våra algoritmer på ett enkelt navigationsproblem där vi erhåller styrlagar av låg komplexitet som instämmer med intuition. I den andra delen av denna avhandling behandlar vi reglersystem där en människa interagerar med systemet och studerar hur mänskligt beslutsfattande i sådana system kan förutspås. Först studerar vi hur interaktionen mellan en maskin och en människa i ett reglersystem can förutspås med hjälp av spelteori, med fokus på en självkörande lastbilskonvoj som interagerar med en mänskligt styrd bil. Interaktionen är modellerad som ett hierarkiskt dynamiskt spel, där den hierarkiska indelningen är tidsmässig med en högupplöst taktil horisont som förutspår omedelbara interaktioner samt en lågupplöst strategisk horisont som estimerar långtgående interaktioner. Indelning möjliggör beräkningar som vi validerar via simuleringar där vi får situations-medvetet beteende med naturliga och säkra interaktioner. För det andra söker vi en model med få parametrar som förklarar mänskligt beteende där vi fokuserar på omkörningar. Vi formaliserar omkörningsproblemet som ett beslutfattningsproblem med perceptuell osäkerhet. Vi presenterar och analyserar numeriskt risk-agnostiska och risk-medvetna beslutsmodeller som avväger om en omkörning är önskvärd. Vi visar hur en förares beslutstid och konfidensnivå kan karakteriserar via två modellparametrar som tillsammans representerar mänskligt risk-beteende. Vi beskriver en experimentell testbädd och presentar preliminära resultat från två mänskliga förare. / <p>QC 20230523</p>